Systems and Methods for Modeling 3D Geological Structures

ABSTRACT

Systems and methods for modeling a three-dimensional (3D) geological structure to improve maximum continuity interpolation. An integration method describes local anisotropic effects and introduces interpolation techniques to perform the interpolation between two points of interest along a direction of maximum continuity and across fault surfaces.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/595,773, filed on Aug. 27, 2012, which is hereby incorporated byreference. U.S. patent application Ser. No. 13/595,773 is a continuationof U.S. Ser. No. 12/710,253, filed on Feb. 22, 2010, is hereby claimedand the specifications thereof are incorporated herein by reference.Applicant therefore, claims priority based on the filing date of U.S.Pat. No. 8,274,859.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

Not applicable.

FIELD OF THE INVENTION

The present invention generally relates to modeling three-dimensional(3D) geological structures. More particularly, the present inventionrelates to an integration method for maximum continuity interpolation in3D geological modeling.

BACKGROUND

Conventionally, geostatistical software describes the pattern of spatialvariation in geological properties (e.g. porosity and permeability)using a variogram model that quantifies the average of expectedvariability as a function of distance and direction. In reservoirs,where the geological characteristics are very continuous and easilycorrelated from well to well, the range (or scale), of correlation willbe large and in reservoirs, where the geological characteristics changequickly over short distances, the range (or scale) of correlation willbe smaller.

In certain geological environments, the range of correlation may bedirectionally independent. This phenomenon is very common in sedimentaryenvironments, especially in those where the primary mechanism oftransport during sediment deposition is wind or water, which results inhighly channelized structures such as deltaic channels, fluvialdeposits, turbidites and the like. These environments usuallydemonstrate a large degree of correlation variation between directionsalong the channel axis and perpendicular to the channel axis. Theprinciples of conventional geostatistical practice, embedded in themajority of commercial tools for geological modeling presently availableon the market, require selection of a single direction of maximumcontinuity, which is an average for the entire domain under study.

Traditional reservoir modeling techniques use simplified two-pointstatistics to represent geological structures with complex geometricalconfigurations, such as deltaic channels, fluvial deposits, turbiditesand shale drapes. The two-point correlation is modeled through thedefinition of a variogram, which makes the description of theabove-mentioned structures highly challenging if not impossible. Onebenefit of two-point geostatistical methods is their speed. Onetechnique, for example, utilizes the Fourier-filter based method, whichis described in an article written by M. Maucec, et. al. called“Streamline-based History Matching and Uncertainty: Markov-Chain MonteCarlo Study of an Offshore Turbidite Oil Field,” and is capable ofgenerating a new realization of permeability field with large numbers ofvariables (˜10⁶) within a few seconds. Although this technique is moreefficient than traditional well-known algorithms, like Choleskydecomposition, it is not suitable for integration into workflows fordynamic inversion and automated history matching of reservoir modelsdue, in part, to its dependence on the use of variogram definition.

Within the last decade, advances have been made in the form ofmulti-point geostatistics (MPS). MPS technology uses correlationsbetween multiple locations at the same time to reproduce volume-variancerelationship and model realizations, which are conditioned to localsample data. Examples of MPS technology combine codes like SNESIM andS-GeMS. The latter, for example, is dedicated to the local optimizationof parameters involved in variogram-based models to take into accountlocal structural characteristics of the data. MPS technology, however,still has its disadvantages such as, for example: a) dependence on thetraining image or training data set; and b) very long computationaltimes for generating new geological model/realization.

More recently, Landmark Graphics has developed technology forthree-dimensional volumetric modeling of geological properties using aMaximum Continuity Field (MCF). This technology is more commonlyreferred to as Point Vector technology, which is described inInternational Patent Application Publication No. WO2009/151441 and isincorporated herein by reference. The Point Vector technology introducesseveral advantages that enable a user to: i) direct control over localcontinuity directions; ii) interactively operate with “geologicallyintuitive” datasets, such as layering intervals, projection maps andhand drawings through a MCF; and iii) retain the maximum fidelity of ageological model by postponing the creation of a grid/mesh until thefinal stage of static model building immediately before integrating thestatic model into a dynamic model (reservoir simulator). The reservoirproperty modeling does not need a standard grid but only the correctdistance between the points to estimate/simulate the property and thedata around it.

The current Point Vector technology basically introduces a solution,commonly referred to as an “80% solution,” which is based on theapproach of simply reorienting the axes of a variogram model to thelocal direction specified by the user. In geological structures with ahigh degree of local anisotropy (e.g. meandering channels), thedirection of maximum continuity significantly changes locally for highlymeandering channels. The 80% solution has no way of knowing how to lookbeyond the channel corner. The estimation of the correct distance insuch geological structures requires the introduction of curvilineardistances because the minimum distance between two points in geologicalformations is not always a straight line (i.e. Euclidean distance) andmay be curvilinear—depending on the local anisotropy field. Theremaining challenges are: i) how to calculate the shortest distancebetween two points of interest in a grid-less model of a geologicalstructure; and ii) which direction/orientation to use to correctlydescribe the local anisotropy effects.

SUMMARY OF THE INVENTION

The present invention meets the above needs and overcomes one or moredeficiencies in the prior art by applying an integration method tocalculate the shortest distance between two points of interest in agrid-less model of a geological structure and to determine whichdirection/orientation to use for correctly describing the localanisotropy effects.

In one embodiment, the present invention includes a method for modelinga three-dimensional (“3D”) geological structure, which comprises: i)calculating a structure and diffusion tensor field for a digitized imageof the geological structure; ii) processing the digitized image to forman enhanced image; iii) calculating a fault displacement field for thedigitized image using a computer processor; and iv) merging thestructure and diffusion tensor field, the enhanced image and the faultdisplacement field for interpolation.

In another embodiment, the present invention includes a non-transitorycomputer readable medium tangibly carrying computer executableinstructions for modeling a three-dimensional (“3D”) geologicalstructure, the instructions being executable to implement: i)calculating a structure and diffusion tensor field for a digitized imageof the geological structure; processing the digitized image to form anenhanced image; iii) calculating a fault displacement field for thedigitized image; and iv) merging the structure and diffusion tensorfield, the enhanced image and the fault displacement field forinterpolation.

Additional aspects, advantages and embodiments of the invention willbecome apparent to those skilled in the art from the followingdescription of the various embodiments and related drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described below with references to theaccompanying drawings in which like elements are referenced with likereference numerals, and in which:

FIG. 1 is a flow diagram illustrating one embodiment of a method forimplementing the preset invention.

FIG. 2 is a flow diagram illustrating one embodiment of a method forimplementing step 106 in FIG. 1.

FIG. 3 is a schematic illustration of a maximum continuity vector.

FIG. 4A is a schematic diagram illustrating the application of PointVector technology for tracing a Maximum Continuity Field over a faultline.

FIG. 4B is a seismic data image illustrating the loss of directionalinformation as a result of using the Point Vector technology.

FIG. 5A is a seismic data image illustrating a fault displacement fieldcalculated according to step 205 in FIG. 2.

FIG. 513 is an enlarged image of the area circled in FIG. 5Aillustrating the directional information for the fault displacementfield.

FIG. 6 is a schematic diagram comparing Curvilinear distance andEuclidean distance for illustrating step 108 in FIG.

FIG. 7 is a block diagram illustrating one embodiment of a system forimplementing the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The subject matter of the present invention is described withspecificity, however, the description itself is not intended to limitthe scope of the invention. The subject matter thus, might also beembodied in other ways, to include different steps or combinations ofsteps similar to the ones described herein, in conjunction with othertechnologies. Moreover, although the term “step” may be used herein todescribe different elements of methods employed, the term should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless otherwise expressly limited by thedescription to a particular order.

Method Description

The following description includes one or more methods (hereinaftergenerally referred to as an “integration method”) for integrating thePoint Vector technology and curvilinear point-to-point (CPP)interpolation techniques, which are well known in the art, through dataabstraction to merge a broad range of available datasets and structures.An image digitization platform is disclosed with a generic I/O datastream, which is unified for interpolation. The integration methodprovides a unique tool in the field of 3D geological modeling.

Referring now to FIG. 1, a flow diagram illustrates one embodiment of amethod 100 for implementing the present invention. The method 100represents an integration method for 3D geological modeling, whichaccounts for multiple sources and types of geological and structuralinformation. Such information may include, for example, intuitive handdrawings of structural frameworks and high-resolution seismic dataimages.

In step 102, structural information such as, for example,high-resolution seismic data, may be selected as input data for themethod 100 using the client interface and/or the video interfacedescribed in reference to FIG. 7. The seismic data may containstructural information used for CPP interpolation and structure-orientedsmoothing.

In step 104, other types and formats of geological and structuralinformation such as, for example i) layering intervals (104 a), whichrepresent a vertical space bounded by 2 geological surfaces; ii) mapviews (104 b) of channel limits; and iii) intuitive hand drawings (104c) of structural frameworks (e.g. vectors in a specific portion of avolume-of-interest) may be selected as input data for the method 100using the client interface and/or the video interface described inreference to FIG. 7. The geological and structural information selectedin step 104 may be based on a single direction of maximum continuityusing a Maximum Continuity Field in the manner described inInternational Patent Application Publication No. WO2009/151441 A1. Bydefinition, the direction of maximum continuity is the direction alongwhich the property of interest is the most likely to stay the same whenmoving away from the location of the vector along the direction of thevector as illustrated in FIG. 3. In other words, it defines implicitrelations between locations in the geological model for propertymodeling purposes.

In FIG. 3, a maximum continuity vector 302 has a location 304, amagnitude, a direction and a correlation (length) 306. The correlation(length) of the vector 302 coincides with the long range of thevariogram model. To preserve the vector information in channelized,highly meandering geological structures with a high degree of localanisotropy, the axes of the variogram model could be intuitivelyreoriented to the specified local direction in the manner proposed inInternational Patent Application Publication No. WO20091151441 A1.

In step 106, data abstraction is performed on the input data selected insteps 102 and/or 104 in the manner further described in reference toFIG. 2. Data abstraction is performed because the input data selected insteps 102 and/or 104 usually appear in very different modes orresolution and, in order to deploy the input data during theinterpolation step 108, a generalization or abstraction of the inputdata is required.

In step 108, CPP interpolation such as, for example, natural-neighborinterpolation or image-guided-blended-neighbor interpolation isperformed on the results from step 106. In one embodiment, theinterpolation applies a two-step blending of tensor field data: 1) anyanisotropic Eikonal equation, which is well known in the art, is solvedfor the minimum travel time from a sampled point to a known data point(i.e. well data/location) by methods well known in the art such as, forexample, the Dijkstra-based Fast Marching Method (FMM); and 2) aniterative conjugate-gradient method, which is well known in the art, isused to solve for a blended neighbor interpolant, which is a derivationof a pressure equation. The integration of an underlyingstructure/diffusion tensor field and structure-oriented anisotropysmoothing postulates an important analogy between the Point Vectortechnology and CPP interpolation: correlation (length or range) of amaximum continuity vector is proportional to the maximum eigenvalue ofdiffusion tensor D, which is aligned with the structural orientation(i.e, dominant direction of structure tensor or local lineardiffusivity). The main idea is to align the principal axes of diffusionwith the orientation of the image. The diffusion tensor D should beconstructed such that its eigenvectors follow local orientation of theimage.

When applied to geological modeling, standard, Euclidean-based,point-to-point interpolation of reservoir properties could yielderroneous results because complex-underlying geological structures (e.g.channels) are not honored. In other words, Euclidean-based interpolationdoes not honor sinuosity, which is a prevalent feature of a meanderingchannel. By blending the tensor field data (e.g. structure/diffusion) toguide the interpolation, a major improvement in the physical accuracy ofgeological modeling is achieved. As illustrated in FIG. 6, this isachieved by solving the curvilinear distance 604. The curvilineardistance 604 represents a constrained Euclidean distance between twopoints ( x, y) along an optimal trajectory. The curvilinear distance 604may be solved using: t( x, y)<t_(m)( y) where t and t_(m) represent thetime (t) at ( x, y) and the minimal travel time (t_(m)) at ( y),respectively. Optionally, the curvilinear distance 604 may be solvedusing the solution to the anisotropic Eikonal equation for a minimumtravel time (t_(m)) along the optimal trajectory. The Euclidean distance602 solves for the linear distance between two points ( x, y) and isinaccurate for channelized features.

To date, CPP interpolation has only been implemented using a seismicdata image as the underlying structural input. The integration methodtherefore, brings together other types and formats of geological andstructural data (e.g. layering intervals, projection maps and handdrawings) as the input data for interpolation.

Referring now to FIG. 2, a flow diagram illustrates one embodiment of amethod 200 for implementing step 106 in FIG. 1.

In step 201, an image of a geological structure represented by the inputdata is digitized using any standard method well known in the art forrepresenting an image by a discrete set of its points or samples suchas, for example, rasterizing or image compression. The fundamentalfeature is to render a generic image format compatible withinterpolation in step 108.

In step 202, the digitized image from step 201 is processed usingintelligent point densification (IPoD). IPoD is an improvement of theatomic-meshing techniques described in U.S. Pat. No. 7,050,612, which isincorporated herein by reference. IPoD is generally used to: 1)initialize the sample point location by filling the space spanned by thedigitized image with a pseudo-regular lattice of points, where thenominal distance between the point and its nearest neighbors variesconsistently with the density of structural features in the digitizedimage; and 2) optimize the sample point location by moving the pointswithin the space spanned by the digitized image to minimize a totalpotential energy, defined to be a weighted sum of a point potentialenergy for each sample point and a potential energy for the digitizedimage. The details of the sample point initialization and potentialenergy minimization algorithms are given in U.S. Pat. No. 7,050,612. Theadvantages of IPoD over standard atomic meshing is that it is usedstrictly for the initialization and optimization of sample pointlocation. In this manner, the step of generating the actual triangulatedmesh is omitted. The standard atomic meshing method connects theoptimized sample points location by triangulation, which may be Delaunaytriangulation or any other standard technique for triangulation, that iswell known in the art, to form a mesh of sample points. However,generating a triangulated mesh is a computationally demanding procedureand may require special rendering techniques to correct for artifacts asdescribed in the article written by A. Rueger and D. Hale called“Meshing for Velocity Modeling and Ray-Tracing in Complex VelocityFields,” According to Point Vector technology, the sample points aregenerated on a regular square grid and are randomly or evenlydistributed within the volume-of-interest (VOI). Because IPoD iscomputationally less demanding than atomic meshing, it is anticipatedthat IPoD will generate about 1/300th the number of sample points thangenerated using the Point Vector technology. On a typical seismic dataimage, this can result in an increase in computational speed by a factorof 300.

In step 203, a structure and diffusion tensor field is calculated forthe digitized image from step 201 using methods well known in the art.The individually calculated tensors for the tensor field arc linked withthe corresponding sample point initialized and optimized in step 202 byIPoD. If the technique applied is the “nearest neighbor” search, thenthe tensor is linked to the nearest neighbor sample point. If thetechnique applied is the “natural neighbor” search, then the tensor islinked to the natural neighbor sample point. Both the nearest-neighborsearch and natural-neighbor search techniques are well known in the art.The metric tensor field is the link between distance and time andrepresents the coherence, orientation and dimensionality of features inthe image, which guide the interpolation in step 108. The tensor fieldalters interpolation so that known sample values within spatiallycoherent image features are given more weight than values on oppositesides of such features or where the image is less coherent. Anyunderlying tensor field needs some underlying guiding representation ina computer such as, for example, a drawing, photograph, or other image.If derived from a seismic data image, the tensor field might be sampledwith resolution of the seismic data image. Alternatively, the tensorfield may be sampled more coarsely/cleverly using, for example, IPoD.This means that the tensor field will be sampled with the lowerresolution only at the image locations determined by the optimizedlocations of the sample points from step 202.

In step 204, the digitized image from step 201 is processed usingstructure-oriented smoothing along structures that are apparent in theimage (i.e., calculated in the form of a structure and diffusion tensorfield in step 203), which enhances structural features and preservesimportant discontinuities such as, for example, faults or channels. Forthis step, a broad spectrum of well-known filtering algorithms may beused such as, for example: i) coherency-enhancing anisotropic filters;ii) structure-oriented interpretation filters (i.e. van Gogh filters);iii) recursive (anisotropic) Gaussian filters; and iv) novelimplementations of bilateral filters.

In step 205, a fault displacement field (FDF) for the digitized imagefrom step 201 is calculated in the manner described below. The FDF ismerged with the results from step 203 and step 204, which is returned tostep 108 in FIG. 1 for interpolation. Traditional seismic interpretationmethods focus on the detection of the fault line in the seismic dataimage through, for example, coherence methods. Calculating an FDF,however, corresponds to tracing the MCF through the fault line (in 2D)or through the fault surface (in 3D). Here, the problem is addressedfrom the perspective of calculating the displacement vector field on thedigitized image and extracting the fault-displacement component of suchfield based on a criterion which, for example, distinguishes between thefault throw and a dipping layer. In this manner, step 205 may be appliedto fault line/surface detection in the seismic data image as well as toany other format of digitized data pertaining to structural information(e.g., layering intervals, projection maps and hand drawings).

The FDF is therefore, calculated by searching for the apparentdisplacement vectors in the image and searching for the locations ofpeaks of local cross-correlations between adjacent, that is vertical,image traces. The FDF constrains fault vectors, which vary smoothlywithin a fault that is in the direction of the fault. As ageneralization to non-vertical faults, a correlation smoothing window isapplied, which is aligned with a lag vector. In other words, smoothingis applied for any lag vector in the direction of that vector byshearing where the correlation is done trace-by-trace and is as fast asif it was done for the vertical faults. Shearing is a well known conceptused in computer graphics algorithms.

Referring now to FIG. 4A, the application of the Point Vector technologyis illustrated for tracing the data-searched region 404 a and theassociated MCF 408 a over a fault line 400 a from region 404 a to region406 a by displaying region 404 a of the search and following the faultthrow vector 402 a to region 406 a. Because this operation has to beperformed on multiple-fault lines in a VOI for the entire MCF, it isextremely time-consuming and hard to efficiently implement in practice.As illustrated in FIG. 4B, which is a 2D seismic data image representingamplitude, the calculated structure and diffusion tensor field,represented by the ellipsoids, does not continue through the fault line.The areas represented by 402 b, 404 b and 406 b reveal the areas wherethe directional information is lost. In other words, as the ellipsoidsbecome less elongated, the information on directionality of the tensorfield is lost on the fault line and discontinues tracing the MCF. Bycalculating the FDF in step 205, tracing the MCF over fault lines isgenerically applicable to any underlying structural representation in adigital image. Step 205 represents an improvement over the Point Vectortechnology, which does not require user pre-defined input for faultvectors and associated fault throws, which are required by the use ofthe Point Vector technology.

Referring now to FIG. 5A, a seismic data image illustrates a faultdisplacement field calculated according to step 205 on amplitude seismicdata.

Referring now to FIG. 5B, an enlarged image of the area circled in FIG.5A illustrates the directional information for the fault displacementfield. For clarity, the directionality information (i.e., vector arrows)are shown in FIG. 5B. The vector arrows represent the calculateddisplacement vectors. It is evident from the absolute length of thevector arrows that the displacement is merely associated with thehigh-gradient-change features (e.g. fault throws), but not with the tiltvariation of horizontal layering where the length of the vector arrowsis reduced. This is the preferred behavior because the displacementvector field is envisioned as the structural property.

The present invention is distinguished from existing technologies on themarket by the integration of: i) Maximum Continuity Fields (MCF); ii)Intelligent Point Densification (IPoD); iii) fault displacement fields(FDF); and iv) CPP interpolation. The integration method of the presentinvention therefore: i) describes the local anisotropy effects byintroducing the Maximum Continuity Field and Fault Displacement Fieldbased on underlying structural information and ii) introduces the CPPinterpolation techniques to perform the interpolation between two pointsof interest along the direction of maximum continuity and across thefault surfaces.

System Description

The present invention may be implemented through a computer-executableprogram of instructions, such as program modules, generally referred toas software applications or application programs executed by a computer.The software may include, for example, routines, programs, objects,components, and data structures that perform particular tasks orimplement particular abstract data types. The software forms aninterface to allow a computer to react according to a source of input.DecisionSpace Earth Modeling (DSEM™), which is a commercial softwareapplication marketed by Landmark Graphics, may be used as an interfaceapplication to implement the present invention. The software may alsocooperate with other code segments to initiate a variety of tasks inresponse to data received in conjunction with the source of the receiveddata. The software may be stored and/or carried on any variety of memorymedia such as CD-ROM, magnetic disk, bubble memory and semiconductormemory (e.g., various types of RAM or ROM). Furthermore, the softwareand its results may be transmitted over a variety of carrier media suchas optical fiber, metallic wire and/or through any of a variety ofnetworks such as the Internet.

Moreover, those skilled in the art will appreciate that the inventionmay be practiced with a variety of computer-system configurations,including hand-held devices, multiprocessor systems,microprocessor-based or programmable-consumer electronics,minicomputers, mainframe computers, and the like. Any number ofcomputer-systems and computer networks are acceptable for use with thepresent invention. The invention may be practiced indistributed-computing environments where tasks are performed byremote-processing devices that are linked through a communicationsnetwork. In a distributed-computing environment, program modules may belocated in both local and remote computer-storage media including memorystorage devices. The present invention may therefore, be implemented inconnection with various hardware, software or a combination thereof, ina computer system or other processing system.

Referring now to FIG. 7, a block diagram of a system for implementingthe present invention on a computer is illustrated. The system includesa computing unit, sometimes referred to a computing system, whichcontains memory, application programs, a client interface, a videointerface and a processing unit. The computing unit is only one exampleof a suitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.

The memory primarily stores the application programs, which may also bedescribed as program modules containing computer-executableinstructions, executed by the computing unit for implementing thepresent invention described herein and illustrated in FIGS. 1-2. Thememory therefore, primarily includes a data input module, an abstractionmodule and a data interpolation module, which enable the methodsillustrated and described in reference to FIGS. 1-2. The data inputmodule includes functionality supporting the input of seismic data,layering/intervals, map views and hand drawings. In other words, thedata input module integrates with DSEM™ and the client/video interfacesto execute the functions described in reference to steps 102 a, 104 a,104 b and 104 c in FIG. 1. The data abstraction module integrates withDSEM™ to execute the functions described in reference to step 106 inFIG. 1. In particular, the data abstraction module includesdigitization, intelligent point densification, structure/diffusiontensors, structure-oriented smoothing and a fault displacement fieldcomponents to execute the functions described in reference to steps201-205 in FIG. 2. The data interpolation module integrates with DSEM™to execute the functions described in reference to step 108 in FIG. 1.

Although the computing unit is shown as having a generalized memory, thecomputing unit typically includes a variety of computer readable media.By way of example, and not limitation, computer readable media maycomprise computer storage media. The computing system memory may includecomputer storage media in the form of volatile and/or nonvolatile memorysuch as a read only memory (ROM) and random access memory (RAM). A basicinput/output system (BIOS), containing the basic routines that help totransfer information between elements within the computing unit, such asduring start-up, is typically stored in ROM. The RAM typically containsdata and/or program modules that are immediately accessible to and/orpresently being operated on by the processing unit. By way of example,and not limitation, the computing unit includes an operating system,application programs, other program modules, and program data.

The components shown in the memory may also be included in otherremovable/nonremovable, volatile/nonvolatile computer storage media orthey may be implemented in the computing unit through applicationprogram interface (“API”), which may reside on a separate computing unitconnected through a computer system or network. For example only, a harddisk drive may read from or write to nonremovable, nonvolatile magneticmedia, a magnetic disk drive may read from or write to a removable,non-volatile magnetic disk, and an optical disk drive may read from orwrite to a removable, nonvolatile optical disk such as a CD ROM or otheroptical media. Other removable/non-removable, volatile/non-volatilecomputer storage media that can be used in the exemplary operatingenvironment may include, but are not limited to, magnetic tapecassettes, flash memory cards, digital versatile disks, digital videotape, solid state RAM, solid state ROM, and the like. The drives andtheir associated computer storage media discussed above provide storageof computer readable instructions, data structures, program modules andother data for the computing unit.

A client may enter commands and information into the computing unitthrough the client interface, which may be input devices such as akeyboard and pointing device, commonly referred to as a mouse, trackballor touch pad. Input devices may include a microphone, joystick,satellite dish, scanner, or the like. These and other input devices areoften connected to the processing unit through a system bus, but may beconnected by other interface and bus structures, such as a parallel portor a universal serial bus (USB).

A monitor or other type of display device may be connected to the systembus via an interface, such as a video interface. A graphical userinterface (“GUI”) may also be used with the video interface to receiveinstructions from the client interface and transmit instructions to theprocessing unit. In addition to the monitor, computers may also includeother peripheral output devices such as speakers and printer, which maybe connected through an output peripheral interface.

Although many other internal components of the computing unit are notshown, those of ordinary skill in the art will appreciate that suchcomponents and their interconnection are well known.

While the present invention has been described in connection withpresently preferred embodiments, it will be understood by those skilledin the art that it is not intended to limit the invention to thoseembodiments. It is therefore, contemplated that various alternativeembodiments and modifications may be made to the disclosed embodimentswithout departing from the spirit and scope of the invention defined bythe appended claims and equivalents thereof.

1. A computer-implemented method for modeling a three-dimensional (“3D”)geological structure, which comprises: calculating a structure anddiffusion tensor field for a digitized image of the geologicalstructure; processing the digitized image to form an enhanced image;calculating a fault displacement field for the digitized image using acomputer processor; and merging the structure and diffusion tensorfield, the enhanced image and the fault displacement field forinterpolation.
 2. The method of claim 1, further comprising digitizingan image of the geological structure represented by input data, thedigitized image comprising multiple sample points, each tensor is linkedwith a respective sample point at an optimized sample point location forthe respective sample point based on a nearest neighbor search or anatural-neighbor search.
 3. The method of claim 2, wherein the digitizedimage is processed using structure-oriented smoothing, which comprisesat least one of coherency-enhancing anisotropic filters,structure-oriented interpretation filters, recursive (anisotropic)Gaussian filters and bilateral filters.
 4. The method of claim 3,wherein calculating the fault displacement field for the digitized imagecomprises searching for apparent displacement vectors in the digitizedimage and searching for locations of peaks of local cross-correlationsbetween adjacent-vertical traces for the digitized image.
 5. The methodof claim 4, wherein calculating the fault displacement field for thedigitized image may be applied to any format of digitized datapertaining to structural information.
 6. The method of claim 1, furthercomprising interpolating the merged structure and diffusion tensorfield, enhanced image and fault displacement field by natural-neighborinterpolation.
 7. The method of claim 1, further comprisinginterpolating the merged structure and diffusion tensor field, enhancedimage and fault displacement field by image-guided-blended-neighborinterpolation.
 8. A non-transitory computer readable medium tangiblycarrying computer executable instructions for modeling athree-dimensional (“3D”) geological structure, the instructions beingexecutable to implement: calculating a structure and diffusion tensorfield for a digitized image of the geological structure; processing thedigitized image to form an enhanced image; calculating a faultdisplacement field for the digitized image; and merging the structureand diffusion tensor field, the enhanced image and the faultdisplacement field for interpolation.
 9. The computer readable medium ofclaim 8, further comprising digitizing an image of the geologicalstructure represented by input data, the digitized image comprisingmultiple sample points, each tensor is linked with a respective samplepoint at an optimized sample point location for the respective samplepoint based on a nearest neighbor search or a natural-neighbor search.10. The computer readable medium of claim 9, wherein the digitized imageis processed using structure-oriented smoothing, which comprises atleast one of coherency-enhancing anisotropic filters, structure-orientedinterpretation filters, recursive (anisotropic) Gaussian filters andbilateral filters.
 11. The computer readable medium of claim 10, whereincalculating the fault displacement field for the digitized imagecomprises searching for apparent displacement vectors in the digitizedimage and searching for locations of peaks of local cross-correlationsbetween adjacent-vertical traces for the digitized image.
 12. Thecomputer readable medium of claim 11, wherein calculating the faultdisplacement field for the digitized image may be applied to any formatof digitized data pertaining to structural information.
 13. The computerreadable medium of claim 8, further comprising interpolating the mergedstructure and diffusion tensor field, enhanced image and faultdisplacement field by natural-neighbor interpolation.
 14. The computerreadable medium of claim 8, further comprising interpolating the mergedstructure and diffusion tensor field, enhanced image and faultdisplacement field by image-guided-blended-neighbor interpolation.